System and method for light optimization

ABSTRACT

The present disclosure relates to a method and related system for spectrum optimization of an illumination light source. Spectrum optimization according to the present disclosure can be based on various optimization parameters, including but not limited to luminous efficacy, color rendering effect, luminous efficacy of radiation, mesopic efficacy of radiation, cirtopic efficacy of radiation, etc. The present method and system are capable of optimizing illumination performance of a light source in various aspects in an individual or integrated manner. Further, the present method and system are capable of accommodating different illumination purposes and conditions by combining and prioritizing different optimization parameters.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/573,510, filed on Nov. 13, 2017, which is U.S. national stage entryunder 35 U.S.C. § 371 of International Application No.PCT/CN2016/080365, filed on Apr. 27, 2016, claims priority to Chinesepatent application No. 201510241210.4, filed on May 13, 2015, andChinese patent application No. 201510493084.1, filed on Aug. 12, 2015,which are hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

The present disclosure relates to the field of lighting technology andparticularly relates to a method and related system for spectrumoptimization of an illumination light source.

BACKGROUND OF THE INVENTION

Artificial lighting is an essential element of modern life. How toachieve ideal effects of artificial lighting has been a hot researchtopic. Many factors are to be considered for optimizing artificiallighting, such as efficiency of energy saving of the lighting solution,safety of an environment under lighting, aesthetics of illuminatedobjects or scenes, and animal's physiological or psychological reactionsto artificial lighting, etc. For example, some studies show thatlong-term exposure under inappropriate illumination spectra could incurhuman health problems, such as the seasonal affective disorder. Researchon human mesopic vision also suggests that in mesopic vision, spectralresponses of human eyes bias towards shorter wavelengths. Thus, asatisfying luminous efficacy under photopic vision conditions may not besufficient or as satisfactory under mesopic vision conditions.

Traditional evaluation on artificial light sources typically focuses onparameters such as luminous efficacy, color rendering and colortemperature. However, as artificial lighting needs in modern lifediversify and with advancement in research fields such as mesopic visionand non-visual biological effect, the traditional parameters quicklybecome insufficient to account for efficiency, comfort, safety, healthconcerns and various other considerations of artificial lighting. Thus,there exists a need in the field for a new artificial lighting solutioncapable of solving the above problems.

SUMMARY

This application relates generally to spectrum optimization of anillumination light source. A method and related system disclosed hereincan provide a destined light under a working condition based on a meritfunction.

In one example, a method for providing artificial lighting under aworking condition is provided. The method includes determining adestined chromaticity of a destined light; selecting one or morecomponent lights, each component light having a suitable componentchromaticity; calculating proportion for each component light based on amerit function, wherein the merit function includes at least oneoptimization parameter having a first functional correlation with theproportion of at least one component light; and combining the one ormore component lights according to the calculated proportion, therebysynthesizing the destined light. In some embodiments, the firstfunctional correlation may be a linear function, an inverse function, anexponential function, a power function or a regular non-linear function.The number of the one or more component lights may be four and thecomponent lights may be monochromatic or polychromatic.

In another example, the proportion of the component lights may assume asecond functional correlation with respect to each other. The secondfunctional correlation may be a linear function or a multivariatefunction. The type of the second correlation function and the firstcorrelation function may be the same, or different.

In a further example, the method includes acquiring information of theworking condition. The information may be one or more selected from thegroup consisting of a reflectance spectrum of a target object, colorappearance of a target object under the artificial lighting, a conditionof an ambient environment, and a purpose of the artificial lighting. Thereflectance spectrum of the target object depends on a spectrum powerdistribution of the destined light and a spectral reflectance curve ofthe target object.

In still a further example, the method includes choosing the at leastone optimization parameter. The at least one parameter may be selectedfrom the group consisting of luminous efficacy, luminous efficacy ofradiation, color rendering index, color temperature, circadian efficacyof radiation, mesopic efficacy of radiation, luminous efficacy inscotopic vision, spectral reflectance luminous efficacy of radiation,photosynthetic photon flux and chromaticity of light reflected by atarget subject under the artificial illumination.

In still a further example, a system for providing an artificiallighting under a working condition is provided. The system includes aplurality of light sources, each of which is capable of emitting acomponent light having a component chromaticity. The system alsoincludes a chromaticity coordinate unit configured to determine adestined chromaticity of a destined light and select one or morecomponent lights of suitable component chromaticity, a calculating unitconfigured to calculate proportion of each selected component lightbased on a merit function. The merit function includes at least onoptimization parameter having a first functional correlation with theproportion of at least one component light. The system further includesa light source driver configured to combine the one or more componentlights according to the calculated proportion, thereby synthesizing thedestined light. The calculation unit may be configured to define asecond functional correlation between the proportions of the componentlights. The system may further include an ambient information obtainingmodule configured for acquiring working condition information. Theworking condition information may include a reflectance spectrum of atarget object, and the ambient information obtaining module includes oneor more sensors configured for detection the reflectance spectrum or auser interface configured for receiving a user input of the workingcondition information. The working condition information includesluminance, color, temperature, weather, climate or time of an ambientenvironment. The calculating unit may be configured to define the firstfunctional correlation and the second functional correlation accordingto the working condition. The chromaticity coordinate unit may beconfigured to determine the destined chromaticity based on chromaticityof light reflected by a target object under the artificial illumination.At least one of the plurality of light sources may be a LED, apolychromatic LED, a multi-packaged LED, a phosphor-converted LED, ahigh pressure sodium lamp or a fluorescent lamp. The light source drivermay be capable of control a magnitude of current or voltage delivered toeach light source, thereby individually controlling an amount ofcomponent light emitted by the corresponding light source.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be more readily understood from the detaileddescription of exemplary embodiments presented below considered inconjunction with the attached drawings, of which:

FIG. 1 illustrates an exemplary lighting system capable of spectrumoptimization according to some embodiments of the present disclosure.

FIG. 2 depicts an exemplary working condition of the lighting systemaccording to some embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating an exemplary spectrumoptimization system according to some embodiments of the presentdisclosure.

FIG. 4A shows the spectral power distributions (SPD) of the componentlights according to some embodiments of the present disclosure.

FIG. 4B shows the chromaticity coordinates of the corresponding colorsaccording to some embodiments of the disclosure.

FIG. 5 illustrates a block diagram of a lighting system according tosome embodiments of the present disclosure.

FIG. 6 is a flowchart illustrating a process for spectrum optimizationaccording to some embodiments of the present disclosure.

FIG. 7 is a flowchart illustrating the process for determiningoptimization parameters and optimizing illumination spectrum accordingto some embodiments of the present disclosure.

FIG. 8 is a flowchart illustrating an exemplary spectrum optimizationprocess that takes into consideration the reflectance spectrum of thetarget object according to some embodiments of the present disclosure.

FIG. 9 illustrates the result of the proportion of each light sourcebased on a determined chromaticity coordinate of the light mixtureaccording to some embodiments of the present disclosure.

FIG. 10 illustrates the simulated spectrum of incandescent lamp andfour-packages LEDs using 11 channels LED cube with fit goodness.

FIG. 11 illustrates the linear fit of SRLER and t of theoretical andsimulated four-package LEDs.

FIG. 12 illustrates the relationship between LE, CRI and the proportionof a light source in a four-package LED system according to someembodiments of the present disclosure.

FIGS. 13A-13C illustrate the mesopic efficacy at different luminancesaccording to some embodiments of the present disclosure.

FIG. 14A-FIG. 14B illustrate the optimization process considering LER,C/P and S/P ratio according to some embodiments of the presentdisclosure.

FIG. 15 illustrates an exemplary embodiment of an artificial lightingsolution for indoor plantations.

FIG. 16 is an embodiment of color mixing of four-package LED atdifferent color temperature.

FIG. 17 shows the proportion of each LED as a function of t for colormixing of four CCTs according to some embodiments of the presentdisclosure.

FIG. 18 shows CRI color samples with their chromaticity coordinatesaccording to some embodiments of the present disclosure.

FIG. 19 shows chromaticity coordinates on eight general color samplesaccording to some embodiments of the present disclosure.

FIG. 20 shows color effect changing under different CCTs light sourcesaccording to some embodiments of the present disclosure.

FIG. 21 shows SRLER as function oft for color mixing of four-package at3000K, 4000K, 5000K and 6000K according to some embodiments of thepresent disclosure.

FIG. 22 shows SRLER as function of t for color mixing of four-package at4000K on a color sample according to some embodiments of the presentdisclosure.

FIG. 23A shows incandescent lamp as light source and light greyish redas color sample according to some embodiments of the present disclosure.

FIG. 23B shows the spectrum power distribution of four LEDs and spectrumreflected from color sample light greyish red according to someembodiments of the present disclosure.

FIG. 24 shows the chromaticity coordinates of incandescent lamp, lightgreyish red and illuminated color according to some embodiments of thepresent disclosure.

FIG. 25 shows the proportion of each LED as a function oft for colormixing of destined target chromaticity according to some embodiments ofthe present disclosure.

FIG. 26 shows the chromaticity coordinates of four-package LEDsfollowing linear function according to some embodiments of the presentdisclosure.

FIG. 27 shows SRLER of spectrum of four-package LEDs as inverseproportion function oft according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF THE INVENTION

Provided herein are methods and systems for spectrum optimization ofartificial illumination (lighting). Particularly, in some embodiments,the methods and systems disclosed herein are capable of adjustingoptical characteristics of the illumination light to accommodateparticular needs and/or purposes of the artificial lighting. In someembodiments, the present method and system is capable of automaticallyrecognizing features of an object or environment under illumination. Theobject is hereinafter referred to as the “target object” or “illuminatedobject”; and the environment is hereinafter referred to as the “targetenvironment” or “illuminated environment.” In some embodiments, whendiscussing an environment surrounding an illuminated object, thesurrounding environment is also referred to as the “ambientenvironment.” In various embodiments, an ambient environment may beilluminated or not illuminated by the artificial lighting.

In some embodiments, the present method and system is configured tooptimize the illumination spectrum based on one or more optimizationparameters. In various embodiments, the optimization parameters mayinclude but are not limited to luminance, luminous efficacy (LE),luminous efficacy of radiation (LER), spectral reflectance luminousefficacy of radiation (SRLER), reflectance spectrum of illuminatedobject, color rendering index (CRI), color temperature, chromaticity,mesopic efficacy of radiation, luminous efficacy in scotopic vision, andcircadian efficacy of radiation, photosynthetic photon flux (PPF), etc.The reflectance spectrum denotes the reflection of an object under theillumination of a certain light. The certain light may include but notlimit to reference light. The reflectance spectrum may be expressed asP(λ)ρ(λ), wherein P(λ) is the spectral power distributions (SPD) of theillumination light, and ρ(λ) is the spectral reflectance curve of theilluminated object, ρ(λ) is the spectral reflectance curve of theilluminated object, which equals to the ratio between the reflectedvisible energy and the energy of the illumination light source. Further,various optimization parameters may be selected and determined based onvarious considerations, including but not limited to energy efficiency,luminous efficacy, color effect, biological or physiological,psychological compatibility, health, purpose and environment of thelighting.

Particularly, energy efficiency is an important parameter for evaluatingand providing a lighting solution. As used herein, the term “lightingsolution” refers to a method and/or system that provide illumination.Energy efficiency may be mathematically expressed as the luminousefficacy (LE) and luminous efficacy of radiation (LER) of the lightingsolution. As used herein, LE is a measurement of how efficient alighting solution converts a source energy into visible light energy.Thus, the higher value of LE, the more energy efficient the lightingsolution is. In some embodiments, the source energy may be in the formof electrical power, chemical energy, biological energy or othersuitable forms. Particularly, in some embodiments, LE is defined as theefficiency of energy conversion by an illumination light source, such asthe present lighting system disclosed herein.

Due to the spectral sensitivity of human eyes, not all wavelengths oflight are equally visible to human, or equally effective at stimulatinghuman vision. For example, radiation in the infrared and ultravioletparts of the spectrum is useless for illumination, because they cannotbe seen by human. As used herein, LER is a measurement of the fractionof electromagnetic radiation that is useful for lighting. In someembodiments, LER may be obtained by dividing the luminous flux by theradiant flux. In some embodiments, light of a wavelength ranging from380 nm to 780 nm is visible and useful for lighting. Thus, like LE, LERis also parameter for measuring efficiency of a lighting solution.

Spectrum of an illumination light (illumination spectrum) and thespectrum of the light reflected by target objects (reflectance spectrum)are important considerations for evaluating and providing a lightingsolution. Particularly, the illumination spectrum of the light and thereflectance spectrum of the target object may together determine theatmosphere created by the illumination. For example, in someembodiments, illumination spectrum and reflectance spectrum may togetherdecide luminance of the lighting solution. Particularly, as used herein,luminance refers to a photometric measure of the density of luminousintensity in a given direction, measured in candela per square meter(cd/m²). Thus, luminance measures how a light source works on anilluminated object. In some embodiments, luminance is mathematicallyexpressed by luminous reflectance Y, which is the product of object'sreflectance, the spectral power of a light source, and the luminosityfunction of the CIE standard observer. Thus, luminance is a measurementrelying upon characteristics of both illumination and reflectance.Particularly, if the reflectance spectrum under a lighting systemmatches the reflectance spectrum of this object under equal-energywhite, the lighting solution is likely to produce higher luminance ascompared to when the two spectra mismatch. The reflectance spectrum ofan object under equal-energy white can be defined as referencereflectance spectrum.

As described above, luminance relates to both the illumination sourceand the target object's reflectance. While LER may be used to describeefficiency of an illumination light source, it does not directly relateto reflectance property of the object. Thus, LER is not a precisemeasurement for reflected luminance. That is, from LER alone, one cannotevaluate the illumination efficacy refer to reflectance spectrum,thereby unable to determine whether a satisfactory LER value would leadto also satisfactory luminance of the lighting solution. Accordingly, insome embodiments, another parameter that is similar to LER but alsoconsiders target object's reflectance is used for spectrum optimization.As used herein, spectral reflectance luminous efficacy of radiation(SRLER) refers to the ratio between energy reflected by an illuminatedobject that is visible to human eyes and energy radiated by anillumination source. Thus, the higher SRLER is, the more visible lightis reflected by the target object, and thus the higher luminance of thetarget object. Particularly, in some embodiments, SRLER may bemathematically expressed as:

$\begin{matrix}{{SRLER} = {\frac{\int_{380}^{780}{{P(\lambda)}{V(\lambda)}{\rho (\lambda)}d\; \lambda}}{\int_{380}^{780}{{P(\lambda)}d\; \lambda}} = {\sum\limits_{380}^{780}{{P(\lambda)}{V(\lambda)}{\rho (\lambda)}{{\Delta\lambda}/{\sum\limits_{380}^{780}{{P(\lambda)}{\Delta\lambda}}}}}}}} & (1)\end{matrix}$

where P(λ) is the SPD of the illumination light, V(λ) is the standardluminosity function, ρ(λ) is the spectral reflectance curve of theilluminated object, which equals to the ratio between the reflectedvisible energy and the energy of the illumination light source. Thespectral reflectance curve herein denotes the reflection curve of anobject under the illumination of reference light.

In some embodiments, illumination spectrum and reflectance spectrum maytogether decide also the color appearance of the illuminated object.Particularly, color appearance of an illuminated object or environmentto an observer may be mathematically expressed as the chromaticity ofthe reflected light. In some embodiments, color effect of anillumination solution may be also expressed as the color temperature orcolor rendering index (CRI). Color temperature is conventionallyexpressed in Kelvin, using the symbol K, a unit of measure fortemperature based on the Kelvin scale. Typically, color temperaturesover 5,000K are referred to as cool colors (bluish white), while lowercolor temperatures (2,700 to 3,000 K) are referred to as warm colors(yellowish white through red). Chromaticity is an objectivespecification of the quality of a color regardless of its luminance. Insome embodiments, chromaticity of a light corresponds to a chromaticitycoordinate (x, y) on a standard chromaticity diagram, such as the 1931CIE chromaticity diagram. As used herein, CRI is a quantitativemeasurement of the ability of an illumination light source to revealcolors of an illuminated object faithfully in comparison with an idealor natural light source.

Additionally, human psychological, biological or physiological reactionsto artificial lights are also important considerations in providing alighting solution. For example, spectral sensitivity of human visualperception changes with ambient luminance under a mesopic visioncondition. Particularly, photopic efficacy refers to the averagespectral sensitivity of human visual perception of brightness. In someembodiments, photopic efficacy may be expressed as a ratio of luminousflux for photopic vision to the total luminous flux radiated by anillumination light source. However, spectral sensitivity of human eyesis different from photopic vision in a dark environment. Spectralsensitivity of human eyes may be more precisely measured as mesopicefficacy in a mesopic vision environment, which may occur when luminanceranges approximately from 0.005 to 5 cd/m², and as scotopic efficacy ina scotopic vision environment, which may occur when ambient luminance isbelow 0.005 cd/m². Thus, under mesopic or scotopic vision conditions,illumination effects may be perceived differently from those calculatedunder standard (photopic) conditions. Accordingly, in some embodimentsillumination spectrum may be optimized to account for the mesopic visioneffect. For example, in mesopic vision, spectral responses of human eyesbias towards shorter wavelengths, thus in an mesopic environment, suchas a highway tunnel, illumination using short wavelength lights may bemore efficient in stimulating drivers' vision response and thus keepingthem alert.

Additionally, spectral response of human visual perception also changeswith non-visual physiology of a human body, herein referred to asnon-visual biological effect. For example, in some embodiments, spectralresponse of human visual perception may change with the circadianrhythms of a human body. In other embodiments, human visual perceptionmay in turn affect the circadian rhythms. For example, blue light maysuppress production of the hormone melatonin, leading to increases inalertness at night and reduction in sleep time and quality. In someembodiments, the parameter circadian efficacy of radiation (CER) may beused to measure cirtopic effect of illumination.

Further, purpose and environment of illumination are importantconsiderations in providing a lighting solution. For example,illumination light matching the illuminated environment improvesaesthetic effect of the lighting. For another example, landscapelighting has its own characteristics. Some landscape, like architectureand sculptures, are color saturated and some are not. While landscapesuch as a dense vegetation area is likely color saturated, typicallylandscape has fewer color types. Thus, for landscape lighting, luminanceor color of the light source is less important than luminance or colorreflected by the illuminated environment. For another example, smartlighting, such as mood lighting, provides adjustable lighting atmosphereaccording to human's behavior or mood change. For another example, infunctional lighting, such as for a reading lamp, sufficient luminanceand color contrast tend to make reading comfortable and healthy to humaneyes. For yet another example, in agricultural lighting, photosyntheticphoton flux (PPF) of a light source may be considered. As used herein,the term “photosynthetic photon flux” or “PPF” refers to the ratio offlux for photosynthesis to the number of absorbed photon, thus reflectsthe efficiency of the artificial light solution in stimulating plantgrowth.

According to the present disclosure, spectrum optimization may be basedon one or more optimization parameters. Further, multiple parameters maybe optimized individually or concurrently. In some embodiment, values ofthe one or more optimization parameters may be pre-determined. In otherembodiments, values of the one or more optimization parameters may bedetermined during the spectrum optimization process.

In some embodiments, the present methods and systems optimize thespectrum of an illumination light source by mixing multiple componentlights having desirable characteristics, thereby outputting a destinedlight with a spectrum optimized according to one or more optimizationparameters. As used herein, the term “component light” refers to one ormore of the lights that are to be mixed together, and the term “destinedlight” refers to the light output that has the optimized spectrum.According to the present disclosure, the component light may bemonochromatic or polychromatic. In some embodiment, a component lightmay be produced by a LED, such as but not limited to a polychromaticLED, multi-chip LED, PC LED, a high pressure sodium lamp (HPS),fluorescent lamp (FL), or other optical devices capable of emitting asingle wavelength light or light having a narrow range of spectral powerdistribution (SPD), such as a peak width at half height of smaller than30 nm.

The present systems and methods may find their applications in variousfields, including but not limited to multi-packaged LEDs, aphosphor-converted LED (PC LED), high pressure sodium lamp (EPS),fluorescent lamp (FL), or the like, or a combination thereof.

The following paragraphs will describe the present method and systemmore fully hereinafter with reference to the accompanying drawings inorder to provide a thorough understanding of the relevant disclosure, inwhich preferred embodiments of the invention are shown. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present disclosure. Thus, the invention maybe embodied in many different forms and should not be construed aslimited to the embodiments set forth herein; but to be accorded thewidest scope consistent with the claims.

In one aspect of the present disclosure, provided herein is a system forspectrum optimization. As used herein, the term “system,” “device”,“module”, and/or “unit” are one method to distinguish differentcomponents, elements, parts, section or assembly of different level indescending order. However, the terms may be displaced by otherexpression if they may achieve the same purpose.

FIG. 1 illustrates an exemplary lighting system capable of spectrumoptimization according to some embodiments of the present disclosure. Asshown in the figure, the lighting system 100 may include a lightemitting device 110, a driver 120, a controller 130 and a receiver 140.The light emitting device 110 may include a single light source, or aset of multiple light sources. Light emitted by the emitting device maybe monochromatic having a single wavelength or a narrow SPD with asingle peak, or may be polychromatic having a mixture of differentwavelengths. In some embodiments, the light sources produce componentlights that are to be mixed to produce a destined light having anoptimized spectrum.

The driver 120 may be configured to drive the component light sources inthe light emitting device 110. In some embodiments, the driver 120 maychange the composition of the destined light by adjusting the proportionof a component light.

The controller 130 may control the function of the driver 120. In someembodiments, the controller 130 may include a processor that isconfigured to execute instructions for spectrum optimization in thesystem 100. In some embodiments, the instructions may depend oninformation acquired by the receiver 140. The receiver 140 may beconfigured to acquire different types of information to determine lightemission of the system 100. Exemplary types of information may includeoptical characteristics of a target object and/or conditions of anambient environment acquired by a detector (not shown in FIG. 1), datatransmitted from a local storage device or a remote server, or a manualinput by a user, or the like, or a combination thereof. In someembodiments, the ambient condition may relate to a target object,brightness of surrounding environment, temperate of environment, auser's preference, or the like, or a combination thereof. The datatransmitted from local storage device or a remote server may include aschedule relating to the working condition of the light emitting device110, an instruction relating to the operation of the light emittingdevice 110, or the like, or a combination thereof. Manual input by auser may be performed through a user input interface, such as a wirelessor wire-connected keyboard, a touchscreen with virtual buttons forcommunicating commands and other input information to the lightingsystem 100.

FIG. 2 depicts an exemplary working condition of the lighting systemaccording to some embodiments of the present disclosure. The workingcondition 200 relates to adjusting the illumination spectrum for auser's reading of a book, such that the user's eyesight is protected. Inthis example, the lighting system includes multiple component lightsources for providing illumination on a target object (e.g., a book inthis example). In some embodiments, the illumination light ismonochromatic. In other embodiments, the illumination light may bepolychromatic having RGBA colors or multi-colors of other kinds. In someembodiments, the illumination light is produced by mixing multiplecomponent lights together.

Additionally, the lighting system may include a detector 240 configuredto sense conditions of a target object, such as its color, shape and/orreflectance spectrum. In various embodiments, the detector 240 may bearranged as a unit separated from the light sources. A controller (notshown in FIG. 2) may be configured to optimize illumination spectrum ofthe lighting system to comfort human eyes, based on the informationacquired by the detector, such as the book's reflectance spectrum.Specifically, in the spectrum optimization process, one or moreparameters of the illumination light may be optimized, such aschromaticity, color rendering effect, luminous efficacy, reflectedefficiency, circadian effect, etc. The optimization parameters may bedetermined during the optimization process, based on informationacquired by the detector, or pre-determined or input by a user. Furtherdetails regarding the optimization parameters will be discussed below.

FIG. 3 is a block diagram illustrating an exemplary spectrumoptimization system 300 according to some embodiments of the presentdisclosure. For better illustration, the spectrum optimization system isdescribed with the example of a lighting system having an adjustableillumination spectrum. As shown in FIG. 3, the lighting system 300 mayinclude a light emitting device 310, a light source driver 320, a lightsources calculating module 330, and an input 340. In some embodiments,the light emitting device 310 may include multiple light sources thatmay be monochromatic or polychromatic. In some embodiments, a componentlight source may produce a monochromatic light having a singlewavelength or a narrow SPD with a single peak. In other embodiments, acomponent light source may produce a polychromatic light having multipledifferent peaks in its SPD. In some embodiments, a component lightsource may be any type of light source capable of emitting singlewavelength light or light with a narrow SPD with a single peak, such asa LED, high pressure sodium lamp (HPS), fluorescent lamp (FL), or thelike, or any combination thereof. Of different kinds of light sources,multi-package LEDs are flexible in spectral composition, and spectrumproportions of each LED are easy to control. For example, in someembodiments, by choosing different chips, a variety of LEDs withdifferent spectra could be obtained.

In some embodiments, chromaticity of each light source corresponds to aspecific chromaticity coordinate on a chromaticity diagram, which inturn corresponds to a specific color presented on the chromaticitydiagram. Further details of chromaticity coordinates will be discussedin relation to FIG. 4B.

For example, in some embodiments, the light emitting device 310comprises four component light sources (e.g., four LEDs). As describedabove, each component light source may emit light having a specificcolor. For example, in some embodiments, the four colors may be red,amber, green and blue. In various embodiments, any colors presented onthe chromaticity diagram may be used. A polychromatic destined lighthaving desirable optical characteristics may be produced by mixing thecomponent lights according to certain proportions. In some embodiments,proportions of the component lights may correlate with each other.Particularly, in some embodiments, proportion of one component light mayassume a linear relationship with proportion of another component light.It shall be noted that the above description of the light emittingdevice is provided for illustration purpose, and is not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, various variations and modifications may be conductedunder the teaching of the present disclosure. For example, the lightemitting device 310 may have any number of component light sources, eachlight source may produce a component light of any color, and a componentlight may be a monochromatic or polychromatic light.

The light source driver 320 may drive the light sources by delivering tothem voltage or current at calculated levels. The light source driver320 may receive a command from the light source calculating module 330,and adjust driving voltage or current for individual light sourcesaccordingly. The light source calculating module 330 may be configuredto select and determine parameters for spectrum optimization based oninformation received from the input 340. For example, the light sourcecalculating module 330 may calculate respective proportions of multiplecomponent lights to be combined to generate a destined light having adesirable synthesized chromaticity. In some embodiments, the input 340may provide the light source calculating module 330 informationregarding a working condition of the lighting emitting device 310. Asused herein, the term “working condition” broadly relates to anycondition or circumstance under which a lighting solution operates,which includes but are not limited to the purpose or goal of thelighting, the target object or environment to be illuminated, therequirement or input by a system default or a user, etc. In someembodiments, information regarding the working condition relates toconditions of an ambient environment of a target object and may beacquired by a detector, transmitted from a local storage device or aremote server, or manually input by a user, or the like, or acombination thereof.

In some embodiments, the light source calculating module 330 calculatesrespective proportions of component lights based on the componentchromaticity and the destined chromaticity. As used herein, the term“component chromaticity” refers to the chromaticity of a componentlight, and the term “destined chromaticity” or “synthesizedchromaticity” refers to the chromaticity of the destined light. In someembodiments, the input 340 decides the component and destinedchromaticity and transmits the values to the light source calculatingmodule 330.

Below is an example to illustrate the calculating process. Four LEDs areselected to produce component lights of red, amber, green and bluecolors, respectively. FIG. 4A shows the spectral power distributions(SPD) of the component lights. Particularly, FIG. 4A shows thenormalized SPD of the four LEDs with red, amber, green and blue forcolor mixture and spectrum optimization. The abscissa represents thewavelength, and the ordinate P(λ) represents the SPD. As shown in thefigure, each LED has a narrow range of spectral power distribution and acentral maximum. For example, the LED generating blue light may have aspectrum centralized at 450 nm.

As described above, each color corresponds to a chromaticity coordinate(x, y) on the 1931 CIE chromaticity diagram. Thus, FIG. 4B shows thechromaticity coordinates of the corresponding colors according to someembodiments of the disclosure. As shown in FIG. 4B, points R, A, G and Bcorrespond to the chromaticity coordinates of colors in red, amber,green and blue, respectively. Point X is the intersection point ofpoints R, A, G and B.

In this example, the destined light is set to have a chromaticitycorresponding to point D on the chromaticity diagram. The destined lightis also set to have a desirable color temperature of 4000K. See thefigure showing point D sitting on the Planckian curve representingblackbody radiation of 4000K. If point D falls within an area surroundedby the selected component chromaticity coordinates, the destined lightcan be synthesized by mixing some or all of the selected componentlights.

Particularly in this example, the destined chromaticity D locates withintriangle XGB. Accordingly, the destined light may be generated bycombining the three of green, amber and blue lights, or by combining thethree of green, red and blue lights, or by combining the four of green,amber, red and blue lights. Alternatively, the destined light may begenerated by combining all four of green, red, blue and amber lights.Thus, component light sources may be selected. In various embodiments,the number of component lights can be any number, including 1, 2, 3, 4or greater than 4 component lights.

After deciding the component lights, their respective proportions forgenerating the destined light can be calculated by the color mixturefunction as writing in equation (2) below. Further details regardingspectrum optimization using the color mixture function will be discussedin relation to FIGS. 6 through 8.

In some embodiments, connection between different modules or units maybe in a wired or wireless fashion. The wired connection may includeusing a metal cable, an optical cable, a hybrid cable, an interface, orthe like, or any combination thereof. The wireless connection mayinclude using a Local Area Network (LAN), a Wide Area Network (WAN), aBluetooth, a ZigBee, a Near Field Communication (NFC), or the like, orany combination thereof.

It should be noted that the above description about the lighting systemis merely an example, and should not be understood as the onlyembodiment. Obviously, to those skilled in the art, after understandingthe basic principles of the connection between different modules orunits, the modules or units and connection thereof may be modified orvaried without departing from the principles. The modifications andvariations are still within the scope of the current disclosure. In someembodiments, these modules or units may be independent. In someembodiments, part of the modules or units may be integrated into asingle module or unit to work together.

FIG. 5 illustrates a block diagram of a lighting system 500 according tosome embodiments of the present disclosure. The lighting system 500 mayinclude a light emitting device 510, a light source driver 520, a lightsource calculating module 530 and an ambient information obtainingmodule 550. The light source calculating module 530 may include achromaticity unit 531, an optimization parameter unit 532, a weightfactor unit 533 and a calculating unit 534. The ambient informationobtaining module 550 may include a reflectance spectrum sensing unit 551and an ambient environment sensing unit 552. Under the control of thelight source driver 520, the light emitting device 510 generatesillumination light to illuminate on a target object 560. In someembodiments, the illumination light may be a monochromatic light. Insome embodiments, the illumination light may be a polychromatic light.

In some embodiments, the ambient information obtaining module 550 may beconfigured to acquire and analyze the reflectance spectrum of the targetobject under illumination. In some embodiments, the reflectance spectrumsensing unit 551 may analyze SPD of the reflected light. Reflectancespectrum of the target object may reflect certain opticalcharacteristics of the object. For example, a valley in the SPD at aparticular wavelength may indicate strong absorption of that wavelengthby the object. Similarly, a peak in the SPD at a particular wavelengthmay indicate strong reflection of that wavelength by the object. Also,the reflectance spectrum affects color appearance of the target objectunder illumination. Thus, acquired reflectance information may be usedto set or optimize illumination spectrum of the light emitting device510.

For example, illumination condition may affect plant growth.Illumination spectrum matching a plant's absorbing spectrum is moreefficient to stimulate plant growth and illumination spectrum matching aplant's reflectance spectrum may be used to prevent overgrowth of theplant. Thus, according to different needs, the lighting system maychoose to combine component lights having a desirable wavelength tosynthetize the destined light. As another example, spectrum of landscapelighting may be designed or optimized to match reflectance spectrum ofthe landscape in a natural environment (e.g., under sunlight).Particularly, providing illumination spectrum matching the landscape'snatural reflectance spectrum may make the artificially illuminatedlandscape appear real and vivid. Specifically, for a vegetation areathat strongly reflects green light under the sun, increasing green lightcomponent in an artificial illumination spectrum may help to achieve adesirable lighting effect.

Besides the target object's optical characteristics, the ambientinformation obtaining module 550 may be further configured to acquireand analyze conditions of the target object's ambient environment. Insome embodiments, the ambient environment sensing unit 552 may collectenvironmental information, such as temperature, time, humidity, weather,or the like, or a combination thereof.

In some embodiments, during a spectrum optimization process, thedestined chromaticity coordinate unit 531 of the system may determinethe destined chromaticity of the destined light according to the ambientenvironment. For example, chromaticity for daytime illumination maycorrespond to a higher color temperature, and chromaticity for nighttime illumination may correspond to a lower color temperature. In someembodiments, the destined chromaticity coordinate unit 531 of the systemmay determine an acceptable range of the destined chromaticity.

In some embodiments, the chromaticity unit 531 may further select one ormore component light sources for synthesizing the destined chromaticity,the component light sources each produce light of a particularchromaticity. In some embodiments, the component light sources aremonochromatic, each producing a component light having a singlewavelength or a narrow SPD with a single peak. In other embodiments, thecomponent light sources are polychromatic, each producing a componentlight having multiple peaks in the SPD. In yet other embodiments, somecomponent light sources are monochromatic while other component lightsources are polychromatic.

The optimization parameter unit 532 may determine one or more parametersfor optimizing the illumination spectrum of the light emitting device510. Exemplary parameters may include luminous efficacy (LE), colorrendering index (CRI) and luminous efficacy of radiation (LER), photopicefficacy, mesopic efficacy, circadian efficacy of radiation (CER) fornon-visual biological effects, the spectral reflectance luminousefficacy of radiation (SRLER), photosynthetic photon flux (PPF) or acombination thereof.

The weight factor unit 533 may be configured to operate in connectionwith the optimization parameter unit 532. Particularly, different weightfactors may be given to different optimization parameters. In someembodiments, optimization parameters and their respective weight factorsmay be determined based on the working condition under which thelighting system is used. Merely by way of example, luminous efficacy maybe considered for lighting in an environment where brightness andvisibility are important, such as roads, highway tunnels, manufacturingplants, offices, classrooms, etc. Circadian efficacy may be consideredfor lighting in a human-populated environment, such as bedrooms,hospital wards, offices, and classrooms, etc. Spectral reflectanceluminous efficacy of radiation may be considered for lighting in anenvironment where colored objects need to be illuminated, such as retaillighting, museum lighting, landscape lighting, etc. Color rending may beconsidered for lighting in an environment where discerning colorfulrepresentations is important, such as a painting room, a museum, ashopping mall etc. Mesopic efficacy may be considered for lighting in anenvironment of which the luminance condition may trigger mesopic visionof the human eye, such as highway tunnels and certain outdoorenvironment. Furthermore, multiple parameters chosen for theoptimization may be given different weight factors before composition ofthe destined light is calculated. Also, Table 1 below provides severalexamples for how multiple optimization parameters may be consideredunder different working condition.

TABLE 1 Exemplary requirements of optimization parameters for differentcircumstances Parameter Mesopic Circadian LE CRI efficacy efficacy SRLERCircumstance Requirement Classroom high high very low fair very lowOffice high high very low fair very low Bedroom fair high very low highvery low Shopping mall fair very very low very low very high high Museumfair very very low very low very high high Manufacturing high fair verylow high very low plant Highway very low high high very low tunnel/Roadhigh Landscape fair fair very low very low very high General outdoorhigh fair high fair very low

The calculating unit 534 may be configured to calculate respectiveproportions of component lights to be combined according to theoptimization parameters and weight factors. Further details regardingthe calculation are provided below in relation to FIGS. 6 to 8. Thelight source driver 520 may provide driving current and/or voltage tothe respective component light sources according to the calculationresult, such that the light emitting device 510 produces the destinedillumination light having optimized spectrum.

FIG. 6 is a flowchart illustrating a process 600 for spectrumoptimization according to some embodiments of the present disclosure.

In step 610, information of the working condition may be acquired. Forexample, in some embodiments, information regarding one or more targetobjects and the ambient environment may be acquired. As describedelsewhere in the disclosure, information regarding the target object mayinclude optical characteristics of the object, such as its colorappearance, shape or reflectance spectrum. Information regarding theambient environment may include features such as brightness, dominantcolor, size, temperature, and weather of the environment, or the like,or a combination thereof. In some embodiments, information regarding theworking condition may be acquired by the lighting system 500 through asensor. In some embodiments, the sensor may be integrated in the ambientinformation obtaining module 550 as shown in FIG. 5. In variousembodiments, information regarding the working condition may be inputinto the lighting system 500 by a user or pre-stored in and retrievedfrom a memory of the lighting system 500. For example, in someembodiments, a user may set the lighting system 500 to work for aparticular working condition. In some embodiments, the lighting system500 may have various pre-set modes suitable for working under particularconditions. For example, in some embodiments, the lighting system 500may have a sleep mode, a daytime mode, an energy efficient mode, and abright mode, etc. In some embodiments, the lighting system 500 mayoptimize the illumination spectrum to accommodate circadian effects ofthe human body. In some embodiments, the lighting system 500 mayoptimize the illumination spectrum according to user's customizedrequest. For example, if the user sets the lighting system 500 to theenergy efficient mode, the lighting system 500 may change theoptimization parameters to achieve the highest power efficiency.Specifically, the optimization parameter unit 532 may increase the LEsetting to achieve the energy-saving goal. As another example, if theuser sets the lighting system 500 to the bright mode, the lightingsystem 500 may change the optimization parameters to achieve the bestluminous efficacy.

In step 620, chromaticity of the illumination light may be determined.In some embodiments, the chromaticity may be determined by thechromaticity unit 531 as described in relation to FIG. 5. In someembodiments, the chromaticity may be determined according to the workingcondition information as received in step 610. Alternatively, in otherembodiments, the chromaticity may be input by a user or pre-stored inthe lighting system. For example, a commonly predetermined chromaticityof illumination light is white.

For illustrative purpose, an example of combining four component lightsof particular chromaticity coordinates to generate destined illuminationlight of desirable chromaticity is provided below. According to thecolor mixture function, the relationship between the chromaticitycoordinates of the component lights and that of the producedpolychromatic light can be expressed as:

$\begin{matrix}\left\{ \begin{matrix}{{\left( {{a_{1}l_{1}} + {a_{2}l_{2}} + {a_{3}l_{3}} + {a_{4}l_{4}}} \right)x} = {{a_{1}l_{1}x_{1}} + {a_{2}l_{2}x_{2}} + {a_{3}l_{3}x_{3}} + {a_{4}l_{4}x_{4}}}} \\{{\left( {{a_{1}l_{1}} + {a_{2}l_{2}} + {a_{3}l_{3}} + {a_{4}l_{4}}} \right)y} = {{a_{1}l_{1}y_{1}} + {a_{2}l_{2}y_{2}} + {a_{3}l_{3}y_{3}} + {a_{4}l_{4}y_{4}}}} \\{{a_{1} + a_{2} + a_{3} + a_{4}} = 1}\end{matrix} \right. & (2)\end{matrix}$

where, x₁, y₁; x₂, y₂; x₃, y₃; x₄, y₄ are the chromaticity coordinatesof the component lights; x, y is the chromaticity coordinate of thedestined light; a₁, a₂, a₃, a₄ are the proportions of the componentlights; l₁, l₂, l₃, l₄ are the sum of tri-stimulus values of thecomponent lights. As used herein, the tri-stimulus value refers to theamount of the three primary colors in a tri-chromatic additive colormodel, such as in the 1931 CIE XYZ color space. Equation (2) is to besolved for unknown factors a₁, a₂, a₃ and a₄.

According to the present disclosure, component lights having anychromaticity coordinates may be used in connection with the presentmethod or system. In some embodiments, proportions of the componentlights may correlate with each other. For illustration purpose, supposea₁=t, according to equation (3), a₁ may linearly relate to t.

a _(i) =k _(i) t+b _(i)  (3)

where k_(i) denotes the slope of proportion corresponding to the i^(th)component light with respect to t, b_(i) is a constant corresponding tothe i^(th) component light. Since the chromaticity coordinates ofcomponent lights and destined light may be predetermined, k_(i) andb_(i) may be calculated in equation (2). Also, the range oft is limitedsince a_(i) ranges from 0 to 1.

As shown, in the case where four component lights are used, equation (2)is an underdetermined equation and may have indefinite number ofsolutions for a₁, a₂, a₃ and a₄. Thus, to reach a definite solution, oneor more optimization parameter may be taken into consideration.

In step 630, one or more optimization parameters may be determined by,for example, the optimization parameter unit 532 as shown in FIG. 5.Since the lighting solution is provided for particular workingconditions, the parameter(s) may be optimized accordingly.

As described elsewhere in the disclosure, the optimization parametersmay include but are not limited to color rendering index (CRI), luminousefficacy (LE), luminous efficacy of radiation (LER), mesopic efficacy,efficacy for circadian effects and spectral reflectance luminousefficacy of radiation. The optimization parameters may representdifferent qualities of the lighting solution. In some embodiments, theoptimization parameters may correspond to the proportion of a componentlight (e.g., “t” in equation (3)). Light optimization may be based onone or more parameters. For example, luminous efficacy may be consideredfor lighting in an environment where brightness and visibility areimportant, such as roads, highway tunnels, manufacturing plants,offices, classrooms, etc. Circadian efficacy may be considered forlighting in a human-populated environment, such as bedrooms, hospitalwards, offices, and classrooms, etc. Spectral reflectance luminousefficacy may be considered for lighting in an environment where coloredobjects need to be illuminated, such as retail lighting, museumlighting, landscape lighting, etc. Color rendering effect may beconsidered for lighting in an environment where discerning colorfulrepresentations is important, such as a painting room, a museum, ashopping mall etc. Mesopic efficacy may be considered for lighting in anenvironment of which the luminance condition may trigger mesopic visionof the human eye, such as highway tunnels and certain outdoorenvironment. Furthermore, multiple parameters chosen for theoptimization may be given different weight factors before composition ofthe destined light is calculated. See also Table 1 above.

In step 640, proportions of component lights may be calculated. In someembodiments, the proportions may be calculated by the calculating unit534 as shown in FIG. 5. After determining the optimization parameters instep 630, the optimization process may be performed according to theselected optimization parameters and weight factors.

In step 650, the driver unit 520 may drive the component light sourcesaccording to the proportions determined in step 640.

It shall be noticed that many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. In one example, thesequential order of steps in the flowchart may be adjusted, such thatthe determination of the weight factors of the optimization parametersmay be conducted before acquiring information regarding the workingcondition or determining the chromaticity of the destined light. Inanother example, the step for acquiring of the working conditioninformation may be not necessary, as the chromaticity and wavelengthcomposition of the destined light may be predetermined by a systemdefault, or input by a user.

FIG. 7 is a flowchart illustrating an exemplary process for optimizingillumination spectrum based various parameters according to someembodiments of the present disclosure. In step 720, a destinedchromaticity and one or more optimization parameters may be selected,such as by the optimization parameter unit 532 as shown in FIG. 5. Insome embodiments, one or more of the optimization parameters may relateto the proportion of a component light (t). Particularly, the one ormore optimization parameters may have a certain functional relationshipwith t, such as a linear function, an inverse function, an exponentialfunction, a logarithmic function, a power function and other regularnon-linear function relationship.

In step 731, the illumination spectrum destined light may be optimizedaccording to the parameter of luminous efficacy (LE). In someembodiments, LE of the lighting solution may relate to t. Merely by wayof example, in some embodiments, LE may be expressed as a monotonicincreasing/decreasing function oft.

η=Σ_(i=1) ⁴(k _(i) t+b _(i))η_(i)  (4)

where η_(i) represents the i^(th) component light's LE that relates tothe SPD of i^(th) component light and the photopic spectral sensitivitycurve, k_(i), b_(i) are constants corresponding to the proportion ofi^(th) component light (e.g., t).

In step 732, the illumination spectrum destined light may be optimizedaccording to the parameter of color rendering index (CRI). In someembodiments, CRI of the lighting solution may relate to t. For example,in some embodiments, the CRI may be expressed as a reverse function oft.

In step 733, the illumination spectrum destined light may be optimizedaccording to the parameter of luminous efficacy of radiation (LER). LERmay also be an inverse function expressed by the proportion of a lightsource (e.g., t). For example, in human visible range, LER may beexpressed as:

$\begin{matrix}{{LER} = {{\int_{380}^{780}{{P(\lambda)}{V(\lambda)}d\; {\lambda/{\int_{380}^{780}{{P(\lambda)}d\; \lambda}}}}} = \frac{\begin{matrix}{{t{\int_{380}^{780}{\left( {{k_{1}P_{1}} + {k_{2}P_{2}} + {k_{3}P_{3}} + {k_{4}P_{4}}} \right){V(\lambda)}d\; \lambda}}} +} \\{\int_{380}^{780}{\left( {{b_{1}P_{1}} + {b_{2}P_{2}} + {b_{3}P_{3}} + {b_{4}P_{4}}} \right){V(\lambda)}d\; \lambda}}\end{matrix}}{\begin{matrix}{{t{\int_{380}^{780}{\left( {{k_{1}P_{1}} + {k_{2}P_{2}} + {k_{3}P_{3}} + {k_{4}P_{4}}} \right)d\; \lambda}}} +} \\{\int_{380}^{780}{\left( {{b_{1}P_{1}} + {b_{2}P_{2}} + {b_{3}P_{3}} + {b_{4}P_{4}}} \right)d\; \lambda}}\end{matrix}}}} & (5)\end{matrix}$

where P(λ) is the SPD of the destined light, P₁, P₂, P₃, P₄ are thecorresponding SPD of the component light. V(λ) is the photopic spectralsensitivity curve, λ is the wavelength, k_(i), b_(i) are constantscorresponding to the proportion of i^(th) component light source (e.g.,t).

In step 734, the illumination spectrum destined light may be optimizedaccording to the parameter of mesopic efficacy. In some embodiments, S/Pratio or M/P ratio may be used to evaluate whether a light source has ahigh mesopic efficacy. Particularly, as used herein, the S/P ratiorepresents the ratio between scotopic luminous flux and photopicluminous flux, and may be used to describe how a light source worksunder mesopic conditions. The calculation of the S/P may be expressedas:

$\begin{matrix}{{S/P} = {{\int_{380}^{780}{{P(\lambda)}{V^{\prime}(\lambda)}d\; {\lambda/{\int_{380}^{780}{{P(\lambda)}{V(\lambda)}d\; \lambda}}}}} = \frac{\begin{matrix}{{t{\int_{380}^{780}{\left( {{k_{1}P_{1}} + {k_{2}P_{2}} + {k_{3}P_{3}} + {k_{4}P_{4}}} \right){V^{\prime}(\lambda)}d\; \lambda}}} +} \\{\int_{380}^{780}{\left( {{b_{1}P_{1}} + {b_{2}P_{2}} + {b_{3}P_{3}} + {b_{4}P_{4}}} \right){V^{\prime}(\lambda)}d\; \lambda}}\end{matrix}}{\begin{matrix}{{t{\int_{380}^{780}{\left( {{k_{1}P_{1}} + {k_{2}P_{2}} + {k_{3}P_{3}} + {k_{4}P_{4}}} \right){V(\lambda)}d\; \lambda}}} +} \\{\int_{380}^{780}{\left( {{b_{1}P_{1}} + {b_{2}P_{2}} + {b_{3}P_{3}} + {b_{4}P_{4}}} \right){V(\lambda)}d\; \lambda}}\end{matrix}}}} & (6)\end{matrix}$

where P(λ) is the SPD of the destined light, V′M is the scotopicspectral sensitivity curve, λ is the wavelength, k_(i), b_(i) areconstants corresponding to the proportion of i^(th) component lightsource. As shown, the S/P may be an inverse function expressed by theproportion of a component light (e.g., t).

The M/P ratio, which may represent LER under mesopic vision, is theratio of mesopic luminous flux to photopic luminous flux, and may bedeemed to represent the mesopic efficacy. Calculation of M/P may beexpressed as:

$\begin{matrix}{{M/P} = {{\int_{380}^{780}{{P(\lambda)}{V_{m}(\lambda)}d\; {\lambda/{\int_{380}^{780}{{P(\lambda)}{V(\lambda)}d\; \lambda}}}}} = \frac{\begin{matrix}{{t{\int_{380}^{780}{\left( {{k_{1}P_{1}} + {k_{2}P_{2}} + {k_{3}P_{3}} + {k_{4}P_{4}}} \right){V_{m}(\lambda)}d\; \lambda}}} +} \\{\int_{380}^{780}{\left( {{b_{1}P_{1}} + {b_{2}P_{2}} + {b_{3}P_{3}} + {b_{4}P_{4}}} \right){V_{m}(\lambda)}d\; \lambda}}\end{matrix}}{\begin{matrix}{{t{\int_{380}^{780}{\left( {{k_{1}P_{1}} + {k_{2}P_{2}} + {k_{3}P_{3}} + {k_{4}P_{4}}} \right){V(\lambda)}d\; \lambda}}} +} \\{\int_{380}^{780}{\left( {{b_{1}P_{1}} + {b_{2}P_{2}} + {b_{3}P_{3}} + {b_{4}P_{4}}} \right){V(\lambda)}d\; \lambda}}\end{matrix}}}} & (7)\end{matrix}$

where P(λ) is the SPD of the destined light, V_(m)(λ) is a combinationof V(λ) and V′(λ), as shown in equation (8). As shown, the M/P may be aninverse function of the spectrum proportion of a component light (e.g.,t).

V _(m)(λ)=mV(λ)+(1−m)V′(λ)  (8)

where m is a coefficient ranging from 0 to 1.

In step 735, the illumination spectrum of the destined light may beoptimized according to the parameter of cirtopic efficiency. Asdescribed elsewhere in the disclosure, the cirtopic effect may relate tothe non-visual biological reaction of a human. In some embodiments, thecirtopic effect may be taken into account in functional lighting. For alighting device in a bedroom, the “sleep mode” may be set to adjust thelight to optimize cirtopic effect. In another example, for trafficlighting, the cirtopic effect may be optimized to keep a driver alert.The C/P ratio, which represents the efficiency for circadian effect, isthe ratio between circadian flux and radiant flux as shown in equation(7), where C (λ) is the circadian action function.

$\begin{matrix}{{C/P} = {{\int_{380}^{780}{{P(\lambda)}{C(\lambda)}d\; {\lambda/{\int_{380}^{780}{{P(\lambda)}{V(\lambda)}d\; \lambda}}}}} = \frac{\begin{matrix}{{t{\int_{380}^{780}{\left( {{k_{1}P_{1}} + {k_{2}P_{2}} + {k_{3}P_{3}} + {k_{4}P_{4}}} \right){C(\lambda)}d\; \lambda}}} +} \\{\int_{380}^{780}{\left( {{b_{1}P_{1}} + {b_{2}P_{2}} + {b_{3}P_{3}} + {b_{4}P_{4}}} \right){C(\lambda)}d\; \lambda}}\end{matrix}}{\begin{matrix}{{t{\int_{380}^{780}{\left( {{k_{1}P_{1}} + {k_{2}P_{2}} + {k_{3}P_{3}} + {k_{4}P_{4}}} \right){V(\lambda)}d\; \lambda}}} +} \\{\int_{380}^{780}{\left( {{b_{1}P_{1}} + {b_{2}P_{2}} + {b_{3}P_{3}} + {b_{4}P_{4}}} \right){V(\lambda)}d\; \lambda}}\end{matrix}}}} & (9)\end{matrix}$

where P (λ) is the SPD of the destined light, C(λ) is the circadianaction function, V(λ) is the photopic spectral sensitivity curve.

As shown, the C/P may be an inverse function of the spectrum proportionof a component light (e.g., t).

In step 736, the illumination spectrum of the destined light may beoptimized according to the parameter of spectral reflectance luminousefficacy (SRLER). As defined in equation (1), SRLER can be expressed as:

${SRLER} = \frac{\int_{380}^{780}{{P(\lambda)}{V(\lambda)}{\rho (\lambda)}d\; \lambda}}{\int_{380}^{780}{{P(\lambda)}d\; \lambda}}$

where P(λ) is the SPD of the destined light, V(λ) is the photopicspectral sensitivity curve, p (λ) is the spectral reflectance curve ofthe illuminated object.

In step 737, the illumination spectrum of the destined light may beoptimized for photosynthetic photon flux (PPF). Particular, PPF can beexpressed by equation (10):

$\begin{matrix}{{PPF} = {{\int_{400}^{700}\frac{{P(\lambda)}\lambda \; d\; \lambda}{n_{A}{hc}}} = {{\sum\limits_{400}^{700}{{P(\lambda)}{{\lambda\Delta\lambda}/n_{A}}{hc}}} = {{\sum\limits_{400}^{700}{\left\lbrack {{\left( {{k_{1}t} + b_{1}} \right)P_{1}} + \ldots + {\left( {{k_{4}t} + b_{4}} \right)P_{4}}} \right\rbrack {{\lambda\Delta\lambda}/n_{A}}{hc}}} = {{\sum\limits_{400}^{700}{\left( {{k_{1}P_{1}} + \ldots + {k_{4}P_{4}}} \right){\lambda\Delta\lambda}\; {t/n_{A}}{hc}}} + {\sum\limits_{400}^{700}{\left( {{b_{1}P_{1}} + \ldots + {b_{4}P_{4}}} \right){{\lambda\Delta\lambda}/n_{A}}{hc}}}}}}}} & (10)\end{matrix}$

where P(λ) is the SPD of the destined light produced by the lightingsolution, n_(A) is the Avogadro constant (μmol⁻¹), h is the Planckconstant, c is speed of light. Another property named photosyntheticradiation flux can be expressed as P_(P)=∫P(λ)Q(λ)dλ, where Q(λ) is thesensitive curve for photosynthesis of plant which is corresponding tothe V(λ) for human. It should be noted that different kinds of plantsmay have different Q(λ), even one plant may have different throughdifferent growing stages.

As shown, the PPF may be a linear function of the spectrum proportion ofa component light (e.g., t).

In step 738, the illumination spectrum of the destined light may beoptimized for the chromaticity light reflected by a target object.Particularly, in some embodiments, chromaticity coordinates of reflectedlight (x_(ρ), y_(ρ)) may be an inverse function of the spectrumproportion of a component light (e.g., t).

In some embodiments, chromaticity of reflected light (x_(ρ), y_(ρ)) maybe derived from equation (11)-(17).

$\begin{matrix}{\mspace{79mu} \begin{matrix}{X_{\rho} = {\int{{P(\lambda)}{\rho (\lambda)}{\overset{\_}{x}(\lambda)}d\; \lambda}}} \\{= {\int{\left\lbrack {{\left( {{k_{1}t} + b_{1}} \right){P_{1}(\lambda)}} + \ldots + {\left( {{k_{4}t} + b_{4}} \right){P_{4}(\lambda)}}} \right\rbrack {\rho (\lambda)}{\overset{\_}{x}(\lambda)}d\; \lambda}}} \\{= {{\left( {{k_{1}t} + b_{1}} \right){\int{{P_{1}(\lambda)}{\rho (\lambda)}{\overset{\_}{x}(\lambda)}d\; \lambda}}} + \ldots + \left( {{k_{4}t} +} \right.}} \\{\left. b_{4} \right){\int{{P_{4}(\lambda)}{\rho (\lambda)}{\overset{\_}{x}(\lambda)}d\; \lambda}}} \\{= {{\left( {{k_{1}t} + b_{1}} \right)X_{\rho 1}} + \ldots + {\left( {{k_{4}t} + b_{4}} \right)X_{\rho 4}}}}\end{matrix}} & (11) \\{\mspace{79mu} \begin{matrix}{Y_{\rho} = {\int{{P(\lambda)}{\rho (\lambda)}{\overset{\_}{y}(\lambda)}d\; \lambda}}} \\{= {{\left( {{k_{1}t} + b_{1}} \right)Y_{\rho 1}} + \ldots + {\left( {{k_{4}t} + b_{4}} \right)Y_{\rho 4}}}}\end{matrix}} & (12) \\{\mspace{79mu} \begin{matrix}{Z_{\rho} = {\int{{P(\lambda)}{\rho (\lambda)}{\overset{\_}{z}(\lambda)}d\; \lambda}}} \\{= {{\left( {{k_{1}t} + b_{1}} \right)Z_{\rho 1}} + \ldots + {\left( {{k_{4}t} + b_{4}} \right)Z_{\rho 4}}}}\end{matrix}} & (13) \\\begin{matrix}{x_{\rho} = \frac{X_{\rho}}{X_{\rho} + Y_{\rho} + Z_{\rho}}} \\{= \frac{{\left( {{k_{1}t} + b_{1}} \right)X_{\rho 1}} + \ldots + {\left( {{k_{4}t} + b_{4}} \right)X_{\rho 4}}}{{\left( {{k_{1}t} + b_{1}} \right)\left( {X_{\rho 1} + Y_{\rho 1} + Z_{\rho 1}} \right)} + \ldots + {\left( {{k_{4}t} + b_{4}} \right)\left( {X_{\rho 4} + Y_{\rho 4} + Z_{\rho 4}} \right)}}} \\{= \frac{{\left( {\sum\limits_{i = 1}^{4}{k_{i}X_{\rho \; i}}} \right) \cdot t} + {\sum\limits_{i = 1}^{4}{b_{i}X_{\rho \; i}}}}{{\left( {\sum\limits_{i = 1}^{4}{k_{i}\left( {X_{\rho \; i} + Y_{\rho \; i} + Z_{\rho \; i}} \right)}} \right) \cdot t} + {\sum\limits_{i = 1}^{4}{b_{i}\left( {X_{\rho \; i} + Y_{\rho \; i} + Z_{\rho \; i}} \right)}}}}\end{matrix} & (14) \\{\mspace{79mu} {y_{\rho} = \frac{{\left( {\sum\limits_{i = 1}^{4}{k_{i}X_{\rho \; i}}} \right) \cdot t} + {\sum\limits_{i = 1}^{4}{b_{i}X_{\rho \; i}}}}{{\left( {\sum\limits_{i = 1}^{4}{k_{i}\left( {X_{\rho \; i} + Y_{\rho \; i} + Z_{\rho \; i}} \right)}} \right) \cdot t} + {\sum\limits_{i = 1}^{4}{b_{i}\left( {X_{\rho \; i} + Y_{\rho \; i} + Z_{\rho \; i}} \right)}}}}} & (15) \\{\mspace{79mu} \left\{ \begin{matrix}{x_{\rho} = {\frac{{a_{x}t} + c_{x}}{{bt} + d} = {\frac{a_{x}}{b} + \frac{\left( {{bc}_{x} - {a_{x}d}} \right)/b^{2}}{t + {d/b}}}}} \\{y_{\rho} = {\frac{{a_{y}t} + c_{y}}{{bt} + d} = {\frac{a_{y}}{b} + \frac{\left( {{bc}_{y} - {a_{y}d}} \right)/b^{2}}{t + {d/b}}}}}\end{matrix} \right.} & (16) \\{\mspace{79mu} {y_{\rho}=={{\frac{{bc}_{y} - {a_{y}d}}{{bc}_{x} - {a_{x}d}} \cdot x_{\rho}} + \frac{a_{y}}{b} - {\frac{a_{x}}{b} \cdot \frac{{bc}_{y} - {a_{y}d}}{{bc}_{x} - {a_{x}d}}}}}} & (17)\end{matrix}$

where X_(ρ), Y_(ρ), Z_(ρ) are the tri-stimulus values of the colorappearance reflected by the object, X_(ρi), Y_(ρi), Z_(ρi) are thetri-stimulus values of the each component light, x(λ), y(λ), z(λ) arethe color matching functions (CMF) of the destined light, P(λ) is theSPD of the destined light produced by the lighting solution, P₁(λ),P₂(λ), P₃(λ), and P₄(λ) are spectral power distribution (SPD) ofrespective four LEDs in a package, ρ(λ) is the spectrum reflectancecurve of the target object, k_(i), b_(i) are constants corresponding tothe proportion of i^(th) component light source, a_(i), c_(i), b, d areconstants. x_(ρ) and y_(ρ) are chromaticity coordinates of reflectedlight, a desired chromaticity coordinate of reflected color appearance(x_(ρ), y_(ρ)) may be acquired by choosing a proper t, so that (x_(ρ),y_(ρ)) may be a property under optimization and can be optimized bygiving a weight factor to the functions of tin equation (19).

As described above, equation (2) may be solved together with anadditional equation based on a selected optimization parameter, suchthat proportions of the component lights (a₁, a₂, a₃ and a₄) may beobtained. In some embodiments, multiple optimization parameters may beconsidered individually. In other embodiments, multiple optimizationparameters may be considered concurrently. Particularly, in someembodiments, multiple optimizations may be given different weightfactors when considered together.

Accordingly, in step 730, multiple optimization parameters may beoptimized concurrently by giving each parameter a weight factor orweight coefficient. Particularly, in some embodiments, a merit functioncan be used for considering multiple optimization parametersconcurrently. In some embodiments, the merit function may be written as:

Merit Function=f(p ₁ ,p ₂ . . . p _(n))  (18)

where n≥1 and p₁, p₂ . . . p_(n) represent the one or more selectedoptimization parameters as described above. The merit function canrepresent the integrated impact on the destined light by variousparameters. In some embodiments, one or more of the optimizationparameters may have no effect on the calculation. In some embodiments,some optimization parameters may have greater effect on the calculationthan other parameters.

More particularly, in some embodiments, a weight factor function can beused for considering multiple optimization parameters concurrently. Forexample, in some embodiments, three parameters LER, C/P and M/P areconsidered for spectrum optimization, and the weight factor function maybe written as:

f(LER,C/P,M/P)=w ₁ f(t)_(LER) +w ₂ f(t)_(C/P) +w ₃ f(t)_(M/P)  (19)

where w₁, w₂, w₃ are weight factors corresponding to LER, C/P and MT,respectively. In various embodiments, weight factors for differentparameters may be the same or different, according to particular needsunder particular working conditions.

It shall be noticed that many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. In someembodiments, the sequential order in which various optimizationparameters are considered may be changed. In some embodiments, someparameters may be left out from the spectrum optimization process. Insome embodiments, the selection of parameters to be optimized may bebased on conditions of the target object and/or its ambient environment.

FIG. 8 is a flowchart illustrating an exemplary spectrum optimizationprocess that takes into consideration the reflectance spectrum of thetarget object according to some embodiments of the present disclosure.

In step 810, information regarding the working condition may beacquired. For example, in some embodiments, the working conditioninformation includes the reflectance spectrum of the target object.

In step 811 the desired color appearance of a target object underillumination of the destined light may be determined. For example, forlandscaping lighting, sometimes reproducing lighting is preferred, whileother times reshaping lighting is preferred. Particularly, in someembodiments, illumination spectrum is designed or optimized such thatappearance of illuminated objects or environment (e.g., landscape) issimilar to that under a natural condition (e.g., under sunlight indaytime). In these embodiments, illumination spectrum of the destinedlight may be optimized to mimic the spectrum of sunlight or white light.In other embodiments, illumination spectrum is designed or optimizedsuch that appearance of illuminated objects or environment (e.g.,landscape) unlike its natural appearance (e.g., simulate an unusualnight effect). In these embodiments, illumination spectrum of thedestined light may be optimized to achieve color enhanced effect forobjects.

In step 820 the chromaticity of the destined light may be determinedbased on the desired chromaticity coordinates of light reflected by theilluminated object or environment. For example, if the color appearanceof a target object under illumination is determined, the color mixingprocess for combining four component lights is similar to the previousexample. The difference is that spectrum reflectance curve of the targetobject ρ(λ) is also considered in the mixing process, and P(λ) isreplaced with P(λ)ρ(λ). Thus, parameters P₁(λ)ρ(λ), P₂(λ)ρ(λ),P₃(λ)ρ(λ), and P₄(λ)ρ(λ) replace the original parameters P₁(λ), P₂(λ),P₃(λ), and P₄(λ), respectively, of the four component lights. Althoughρ(λ) is considered, it is deemed as a modification of the spectralpower, and the intensity proportions of the four component lights a₁ a₂a₃, and a₄ can still be expressed as a_(i)=k_(i)t_(i)+b_(i).Consequently, the deduction of the SRLER is similar to that in equation(3), and the SRLER remains an inverse proportion function of t.

The destined chromaticity (x, y) of the destined light can subsequentlybe quantified with the use of a process similar to the one describedabove. The tristimulus value X of the destined light can be determinedvia equation (20) and the chromaticity coordinate x via equation (21),where x_(i), Y_(i), and Z_(i) represent the tristimulus values of thei^(th) component light, with i ranging from 1 to 4. Similarly, thechromaticity coordinate x is an inverse proportion function of t, as isy, and y is a linear function of x.

$\begin{matrix}\begin{matrix}{X = {\int{{P(\lambda)}{\overset{\_}{x}(\lambda)}d\; \lambda}}} \\{= {\int{\left\lbrack {{\left( {{k_{1}t} + b_{1}} \right){P_{1}(\lambda)}} + {{\ldots \left( {{k_{4}t} + b_{4}} \right)}{P_{4}(\lambda)}}} \right\rbrack {\overset{\_}{x}(\lambda)}d\; \lambda}}} \\{= {{\left( {{k_{1}t} + b_{1}} \right){\int{{P_{1}(\lambda)}{\overset{\_}{x}(\lambda)}d\; \lambda}}} + \ldots + {\left( {{k_{4}t} + b_{4}} \right){\int{{P_{4}(\lambda)}{\overset{\_}{x}(\lambda)}d\; \lambda}}}}} \\{= {{\left( {{k_{1}t} + b_{1}} \right)X_{1}} + \ldots + {\left( {{k_{4}t} + b_{4}} \right)X_{4}}}}\end{matrix} & (20) \\\begin{matrix}{x = \frac{X}{X + Y + Z}} \\{= \frac{{\left( {{k_{1}t} + b_{1}} \right)X_{1}} + \ldots + {\left( {{k_{4}t} + b_{4}} \right)X_{4}}}{{\left( {{k_{1}t} + b_{1}} \right)\left( {X_{1} + Y_{1} + Z_{1}} \right)} + \ldots + {\left( {{k_{4}t} + b_{4}} \right)\left( {X_{4} + Y_{4} + Z_{4}} \right)}}} \\{= \frac{{\left( {\sum\limits_{i = 1}^{4}{k_{i}X_{i}}} \right) \cdot t} + {\sum\limits_{i = 1}^{4}{b_{i}X_{i}}}}{{\left( {\sum\limits_{i = 1}^{4}{k_{i}\left( {X_{i} + Y_{i} + Z_{i}} \right)}} \right) \cdot t} + {\sum\limits_{i = 1}^{4}{b_{i}\left( {X_{i} + Y_{i} + Z_{i}} \right)}}}}\end{matrix} & (11)\end{matrix}$

After determining chromaticity of the destined light, one or moreoptimization parameters may be determined. In some embodiments, multipleoptimization parameters may be considered concurrently by giving eachparameter a weight factor (coefficient), and the proportions ofcomponent lights may be determined in step 840.

In step 850, the driver unit 220 may control the component light sourcesaccording to the proportion determined in step 840.

Similarly to the process described in relation to FIGS. 6 through 8, thepresent method can be used for combining more than four componentlights. For example, for combining n number of component lights,Equation (2) becomes:

$\begin{matrix}\left\{ \begin{matrix}\begin{matrix}{{\left( {{a_{1}l_{1}} + {a_{2}l_{2}} + \ldots + {a_{i}l_{i}} + {\ldots \mspace{14mu} a_{n}l_{n}}} \right)x} =} \\{{a_{1}l_{1}x_{1}} + {a_{2}l_{2}x_{2}} + \ldots + {a_{i}l_{i}x_{i}} + {\ldots \mspace{14mu} a_{n}l_{n}x_{n}}}\end{matrix} \\\begin{matrix}{{\left( {{a_{1}l_{1}} + {a_{2}l_{2}} + \ldots + {a_{i}l_{i}} + {\ldots \mspace{14mu} a_{n}l_{n}}} \right)y} =} \\{{a_{1}l_{1}y_{1}} + {a_{2}l_{2}y_{2}} + \ldots + {a_{i}l_{i}y_{i}} + {\ldots \mspace{14mu} a_{n}l_{n}x_{n}}}\end{matrix} \\{{a_{1} + a_{2} + \ldots + a_{i} + {\ldots \mspace{14mu} a_{n}}} = 1}\end{matrix} \right. & (2)^{\prime}\end{matrix}$

where, x_(i), y_(i) is the chromaticity coordinate of the i^(th)component light; x, y is the chromaticity coordinate of the destinedlight; a_(i) is the proportion of the i^(th) component lights; is thesum of tri-stimulus value of the i^(th) component lights. As usedherein, the tri-stimulus value refers to the amount of the three primarycolors in a tri-chromatic additive color model, such as in the 1931 CIEXYZ color space.

Equation (2)′ is to be solved for unknown factors a_(i). Under thisscenario, to optimize the solution of equation (2)′, for differentworking conditions, the proportions of the different component lightsmay be defined to correlate with one another in various forms. In someembodiments, the particular correlation between proportions of differentcomponent lights may be defined according to practical needs and/orgoals under a particular working condition. In some embodiments, theparticular correlations may be defined according to one or moreoptimization parameters, such as but not limited to luminous efficacy(LE), color rendering index (CRI) and luminous efficacy of radiation(LER), photopic efficacy, mesopic efficacy, circadian efficacy ofradiation (CER) for non-visual biological effects, the spectralreflectance luminous efficacy of radiation (SRLER), photosyntheticphoton flux (PPF) or a combination thereof. In some embodiments, theparticular correlations may affect one or more optimization parameterswith respect to one or more proportions of the component lights.

For example, in some embodiments, proportions of component lights maycorrelate with a multivariate function. Particularly, in someembodiments, the number of variances in the multivariate function varieswith the number of component lights. For example, in some embodimentsthat combine 5 component lights, the multivariate function may bewritten as:

a _(i) =k _(i1) t ₁ +k _(i2) t ₂ +c _(i)  (3)′

where k_(i1) denotes the proportion corresponding to the i^(th)component light with respect to t₁, k_(i2) denotes the proportioncorresponding to the i^(th) component light with respect to t₂, andc_(i) is a constant corresponding to the i^(th) component light.

In other embodiments, proportions of some component lights may correlatein one particular form, while proportions of other component lights maycorrelate in a different form. Such as,

$\begin{matrix}\left\{ \begin{matrix}{{a_{1} + a_{2}} = 0.5} \\{{a_{3} + a_{4} + a_{5}} = 0.5}\end{matrix} \right. & (3)^{''}\end{matrix}$

where a₁, a₂, a₃, a₄, and as are proportions of five different componentlights respectively.

It should be noted that the above description about the functionalcorrelations between proportions of different component lights is merelyexemplary and for illustrative purposes, and should not be understood asthe only embodiments. Various modifications to the disclosed embodimentswill be readily apparent to those skilled in the art, and the generalprinciples defined herein may be applied to other embodiments andapplications without departing from the spirit and scope of the presentdisclosure.

EXAMPLES

The following examples are for illustrative proposes only and should notbe interpreted as limitations of the claimed invention. There are avariety of alternative techniques and procedures available to those ofordinary skill in the art which would similarly permit one tosuccessfully perform the intended invention.

Example 1: Relationship Between the Proportions of the LED Sources inApplication of Four-Package LED for High Performance

FIG. 9 illustrates the result of the proportion of each light sourcebased on a determined chromaticity coordinate of the light mixtureaccording to some embodiments of the present disclosure.

Four different LEDs may be used as light sources. The color of the LEDsmay be red, amber, green and blue. Their chromaticity coordinates may beR (0.6849, 0.3151), A (0.6046, 0.3953), G (0.1221, 0.5706) and B(0.1496, 0.0421). The color-mixing point used herein may have a colortemperature of 4000K with chromaticity coordinates (0.3805, 0.3768).Suppose the proportion of the amber LED to be coefficient t, the otherLED proportions may be solved from equation (3). The results is shown asequation (22).

$\begin{matrix}\left\{ \begin{matrix}{{f(t)}_{R} = {{{- 1.0848}t} + 0.571563}} \\{{f(t)}_{A} = t} \\{{f(t)}_{G} = {{{- 0.0415}t} + 0.33579}} \\{{f(t)}_{B} = {{0.126302t} + 0.092646}}\end{matrix} \right. & (22)\end{matrix}$

where f(t)_(R), f(t)_(A), f(t)_(G), f(t)_(B) may be the proportions ofred, amber, green and blue LED as a function of t. The slopes of thefour linear functions are different. As shown, the proportion of greenLED or blue LED changes slowly with respect to t, and the proportion ofred LED changes fast with respect to t.

Incandescent lamp and 7 kinds of four-package LEDs with different tvalues derived from equation (20) are used in the experiment. Simulatedspectrums are shown in FIG. 10 but limited to the LED cube.Corresponding inverse proportion function of four-package LEDs on redand blue samples are y=(−0.177t+0.620)/(−0.177t+7.82) andy=(0.183t+0.430)/(−0.177t+7.817) respectively. However, in a smallrange, they are very approximate to linear function as shown in FIG. 11.

Example 2: Relationship Between the Optimization Parameters and theProportion of a Light Source in a Four-Package LED System

FIG. 12 illustrates the relationship between LE, CRI and the proportionof a light source in a four-package LED system comprising red, amber,green and blue LEDs. The proportion of the amber LED may be supposed tobe t. The chromaticity coordinates of the four LEDs may be R (0.6849,0.3151), A (0.6046, 0.3953), G (0.1221, 0.5706) and B (0.1496, 0.0421).The color-mixing point used here may have a color temperature of 4000Kwith chromaticity coordinates (0.3805, 0.3768).

The target of the optimization may be to achieve high LE and good colorrendering. As shown, the LE may be a linearly monotonicallyincreasing/decreasing function of proportion for all the combinations offour-package LEDs. As for LE, relative value would be of concern in theembodiment, and may be normalized to its maximum value of 100 lm/W. TheCRI may range from 0 to 100 in terms of the general CRI (Ra). Aftercalculate the LE and the CRI (Ra) as a function of t, the results may beseen in FIG. 10. For LE, the result may be a linear function; while forCRI, the result may be a single peak function of proportion.

For different applications, requirements may be different foroptimizations. Merely by way of example, for outdoor lighting, a CRI ofover 50 may be good enough, and the higher the effective LE the better.So the coefficient t may be around 0.5. For indoor lighting, high colorrendering may be needed. Considering the requirement of LE, thecoefficient t may be set to 0.3.

FIG. 13A, FIG. 13B and FIG. 13C illustrate the mesopic efficacy atdifferent luminances.

In some embodiments, the light sources of the lighting system 500 are R(0.6849, 0.3151), A (0.6046, 0.3953), G (0.1221, 0.5706) and B (0.1496,0.0421). The proportion of the amber LED may be supposed to be t.

Under mesopic vision, the mesopic spectral sensitivity curve may be acombination of photopic and scotopic spectral sensitivity curves asshown in equation (8). The coefficient m equals 0 forL_(mes)≤0.005cd/m²; m=0.767+0.3334 log(L_(mes)) for0.005cd/m²<L_(mes)<5cd/m²; m=1 for L_(mes)≥0.005cd/m².

The S/P ratio may be calculated in equation (7) to bef(t)_(S/P)=(0.4702t+3.3820)/(2.2525t+3.1176). The M/P ratio may bemodified by f(t)_(M/P)=(1−m) f(t)_(S/P)+m. For different combinations ofLEDs, there may be one combination where f(t)_(S/P)=1, and f(t)_(M/P)=1at any luminance, which is denoted as the concentrated point in FIG. 13Aand FIG. 13B. Although f(t)_(M/P) may show inverse proportion functions,they would converge to the point where the M/P ratio equals 1. Theconverged point may appear in meaningful regions or not. In engineeringapplications, the inverse proportion functions may have has two types.One is monotonic increasing and the other is monotonic decreasing. Whenboth independent variable and dependent variable are positive, each typemay be divided into three zones as shown in FIG. 13A and FIG. 13B.

At different luminances, different formulas may be deduced. For example,f(t)_(M/P)=f(t)_(S/P) at 0.005cd/m², f(t)_(M/P)=0.8411f(t)_(S/P)+0.1589at 0.015cd/m², f(t)_(M/P)=0.6668f(t)_(S/P)+0.3332 at 0.05cd/m²,f(t)_(M/P)=0.5077f(t)_(S/P)+0.4923 at 0.15cd/m²,f(t)_(M/P)=0.3334f(t)_(S/P)+0.6666 at 0.5cd/m²,f(t)_(M/P)=0.1743f(t)_(S/P)+0.8257 at 1.5cd/m², and f(t)_(M/P)=1 at5cd/m², respectively.

The M/P ratios of different combinations of four-package LEDs at 4000Kat different luminances are shown in FIG. 13C. Compared to the M/P ratioof FIG. 13A and FIG. 13B, these may belong to zone 2, type 1. Atdifferent luminance, the M/P ratio may change over a large range, fromabout 0.85 to 1.1. Such great differences may mean that spectrumoptimization would be very important for energy saving. In thisembodiment, it is obvious that a high M/P ratio with a maximum reachingto about 1.1 leads to high mesopic efficacy, and may be the optimizationresult ignoring other factors.

FIG. 14A-14B illustrate the optimization process considering LER, C/Pand S/P ratio.

In some embodiments, the light sources of the lighting system 500 are R(0.6849, 0.3151), A (0.6046, 0.3953), G (0.1221, 0.5706) and B (0.1496,0.0421). The proportion of the amber LED may be supposed to be t.

LER, C/P ratio and S/P ratio may be expressed by the proportion the LEDs(e.g. t).

f(t)_(LER)=(2.2525t+3.1176)/(−1.766t+7.8167),  (23)

f(t)_(C/P)=(0.5565t+2.4175)/(2.2525t+3.1176),  (24)

f(t)_(S/P)=(0.4702t+3.3820)/(2.2525t+3.1176).  (25)

where f(t)_(LER), f(t)_(C/P), f(t)_(S/P) denotes the LER, C/P ratio andS/P ratio as functions of t.

LER, C/P and S/P as functions of coefficient t of different combinationsof LEDs are shown in FIG. 14A. In some embodiments, different propertiesmay be of different importance, and it may be necessary to weight eachproperty. After all properties have been weighted, equation (26) may beused to optimize the spectrum.

f(LER,C/P,M/P)=w ₁ f(t)_(LER) +w ₂ f(t)_(C/P) +w ₃ f(t)_(M/P)  (26)

where w₁, w₂, w₃ are weight factors corresponding to respectiveoptimization parameters. The sum of all the weights may be equal to 1 tomake different optimizations comparable. Suppose that the four-packageLED lighting system 500 is to be used for road lighting, where LER mayof most importance although the S/P ratio may also important to ensure ahigh mesopic efficacy. While for C/P, the desired value might depend onpopulation density of the area in which the lamps are to be deployed. Ifit is designed to avoid disruption of human sleep patterns, the C/Pratio may be small and have a minus weight. If it is designed to helpkeep drivers' alertness high during nighttime, the C/P ratio may be highand have a positive weight. In this application, the system may bedesigned to keep the drivers alert, so the C/P ratio may be chosen to besmall. The weight coefficients may be set to be w₁:w₂:w₃=0.7:−0.1:0.4.The curve of the function f(LER, C/P, M/P) based on the chosen weightcoefficients is shown in FIG. 14B. It should be noticed that the weightsgiven to different properties not only differ for different embodimentsbut also differ for different design purpose.

Example 3: Selection of Spectrum Optimization Parameters According to aTarget Object's Ambient Environment

FIG. 15 illustrates an exemplary embodiment of an artificial lightingsolution for indoor plantations. Particularly, at initial stages ofplanting, the main purpose is to promote vigorous plant growth. Thus,the illumination spectrum of the lighting solution is set to have adominant proportion of wavelengths that plants absorb forphotosynthesis. Particularly, photosynthetic pigments contained inplants have strong light absorption and characteristic absorptionspectra. For example, chlorophyll absorbs strongly in two regions: 660to 640 nm (red light) and 430 to 450 nm (blue violet light). Absorptionof orange, yellow and especially green light is very little. Caroteneand lutein are different from chlorophyll, they only absorbing visiblelight in the blue violet region

Particularly, in some embodiments involving agricultural lighting, theparameter of photosynthetic photon flux (PPF) may be considered. As usedherein, the term “photosynthetic photon flux” or “PPF” refers to theratio of flux for photosynthesis to the number of absorbed photon, whichcan be expressed by equation (10).

At later stages, plant growth slows down, and the main purpose of theplantation becomes indoor ornamenting. Thus, the illumination spectrumof the lighting solution is adjusted to accommodate different needs. Forexample, the amount of photosynthesis-stimulating lights may be reducedto control plant growth. On the other hand, reflected efficiency, colorrendering, and non-visual biological effect and other aesthetic factorsof the lighting solution may become more important considerations atthis stage. Color of illumination light and that reflected fromilluminated objects together decide the atmosphere created by thelighting solution. For example, illumination light matching theenvironment helps to create a sense of space and improve the lightingeffect. Thus, various ambient conditions, such as time, weather, season,background (ambient) color, may affect the choices of an illuminationspectrum. Particularly, affording weights to the various considerations,such as color rendering, reflected efficiency, non-visual biologicaleffect, and other esthetic factors, a synthesized chromaticitycoordinate may be decided for the destined light, component lightshaving desirable properties (e.g. color and wavelength composition) maybe chosen, and their mixing proportions may be calculated according tothe methods described above. In this way the lighting system 1300 givesa quantitative solution for spectrum optimization of indoor plantationlighting.

Example 4: Spectrum Optimization to Reach Destined Color Temperature

FIG. 16 is an embodiment of color mixing of four-package LED atdifferent color temperature. Four different LEDs, red, amber, green andblue LED are used in the study with their chromaticity coordinates R(0.1496, 0.0421), A (0.1221, 0.5706), G (0.6047, 0.3953), and B (0.6849,0.3151) as shown in FIG. 17. Synthesized correlated color temperature(CCTs) of component light sources are 3000K, 4000K, 5000K, and 6000K,respectively. Linear functions corresponding to the four CCTs are shownin equation (25) to (28), and proportion of each LED as a function oftfor color mixing of the four CCTs are shown in FIG. 17.

CRI color samples with their chromaticity coordinates shown in FIG. 18are taken as illuminated objects in this example. Chromaticitycoordinates as a function oft for color mixing of four-package LED at3000K, 4000K, 5000K and 6000K on eight general color samples are shownin FIG. 19, which conform to linear function at each different colortemperature. Low color temperature light sources make illuminated objectmore biased to warm color, while high color temperature light sourcesmake illuminated object more biased to cold color. Take color effect oncolor sample 9 in FIG. 20 for example, and see that how color effectchange under different CCTs of light sources. With the change of CCT oflight sources, color effect form a color strip, following a same trendwith CCTs. Calculating chromatic aberration in CIE 1976 uv uniform colorspace by transforming chromaticity coordinates from xy chromatic diagramto uv chromatic diagram. Maximum color aberrations ΔC=√{square root over((u₂−u₁)²+(v₂−v₁)²)} of color samples under four-package LEDs withdifferent CCTs are shown in Table 2. Color aberrations vary in arelative large range, and purity does not directly influence them. InFIG. 20, maximum chromatic aberration at 3000K, 4000K, 5000K, and 6000Kare 0.1352, 0.1409, 0.1441, and 0.1459 respectively.

TABLE 2 Maximum color aberration of color samples under four-packageLEDs with different CCTs Maximum color CCT (K) Purity(%) of aberration3000 4000 5000 6000 color sample Color sample 1 0.0482 0.0423 0.03840.0358 22.2 Color sample 2 0.0276 0.0231 0.0205 0.0189 43.7 Color sample3 0.0024 0.0035 0.0041 0.0045 59.3 Color sample 4 0.0298 0.0244 0.02150.0196 20.7 Color sample 5 0.0246 0.0197 0.0170 0.0153 18.5 Color sample6 0.0093 0.0076 0.0070 0.0066 31.1 Color sample 7 0.0376 0.0332 0.02990.0275 21.8 Color sample 8 0.0690 0.0631 0.0582 0.0546 22.3

As deduced above, SRLER is inverse proportion function of t, and SRLERas a function oft for color mixing of four-package LED at 3000K, 4000K,5000K and 6000K are shown in FIG. 21. In the range shown in FIG. 21,SRLER is very likely to be linear function of t. Take color sample 9 forexample, SRLER at 4000K on color sample 9 as inverse proportion functionof t is shown in FIG. 22, when t is in a large range, it conforms toinverse proportion function. However, for parameter tin a smallmeaningful range, make linear fit of SRLER of tin FIG. 22 with adj. R²0.999, it is a perfect linear function. So SRLER and t can be taken aslinear relationship, and maybe more convenient in some applications. Atdifferent CCTs, SRLERs of four-package LED vary a little. CCT of lightsource is not the main factor influence SRLER. It is spectrumreflectance characteristic that influence SRLER much. Color sample 9 ismore saturated than other eight general color samples, and SRLERs ofcolor sample 9 under light sources vary greatly. SRLERs for eightgeneral color samples vary from about 10% to 50%, while variance ofsample 9 reaches to about 100% or more.

$\begin{matrix}\left\{ \begin{matrix}{{f(t)}_{R} = {{{- 1.03043}*t} + 0.660622}} \\{{f(t)}_{A} = t} \\{{f(t)}_{G} = {{{- 0.05567}*t} + 0.312587}} \\{{f(t)}_{B} = {{0.086092*t} + 0.026791}}\end{matrix} \right. & (27) \\\left\{ \begin{matrix}{{f(t)}_{R} = {{{- 1.0848}*t} + 0.571563}} \\{{f(t)}_{A} = t} \\{{f(t)}_{G} = {{{- 0.0415}*t} + 0.33579}} \\{{f(t)}_{B} = {{0.126302*t} + 0.092646}}\end{matrix} \right. & (28) \\\left\{ \begin{matrix}{{f(t)}_{R} = {{{- 1.12218}*t} + 0.510343}} \\{{f(t)}_{A} = t} \\{{f(t)}_{G} = {{{- 0.03878}*t} + 0.340239}} \\{{f(t)}_{B} = {{0.160965*t} + 0.149417}}\end{matrix} \right. & (29) \\\left\{ \begin{matrix}{{f(t)}_{R} = {{{- 1.14826}*t} + 0.467635}} \\{{f(t)}_{A} = t} \\{{f(t)}_{G} = {{{- 0.04006}*t} + 0.338147}} \\{{f(t)}_{B} = {{0.188317*t} + 0.194218}}\end{matrix} \right. & (30)\end{matrix}$

where f(t)_(i) denotes the proportion of i^(th) monochromatic lightsource as function of t.

Example 5: Spectrum Optimization to Reach Destined Chromaticity

In some applications, people are more concerned about color effect ofobjects, and it requires illuminated object to reach destinedchromaticity. In this part, color mixing of four-package LED to reachdestined target chromaticity has been shown. Incandescent lamp as lightsource and light greyish red as color sample have been taken asreference in FIG. 23A. To simulate color effect of light greyish redunder incandescent lamp, use the four LEDs mentioned in Example 4 abovewith spectrum power P(λ), and obtain P(λ)ρ(λ), denoted as P′(λ).Chromaticity coordinates of P′(λ) of four LEDs are R′ (0.67887,0.32113), A′ (0.58663, 0.41336), G′ (0.13769, 0.61965), and B′ (0.14858,0.04404), and chromaticity coordinates of incandescent lamp (0.45437,0.40658), light greyish red (0.3983, 0.34233), illuminated object color(0.51381, 0.39339) are all shown in FIG. 24. Color mixing of R′, A′, G′,and B′ for illuminated object color, and obtain proportion of each LEDas a function oft in FIG. 25 with linear function shown in equation(31). Chromaticity coordinates of all the four-package LEDs are linearfunction relationship as shown in FIG. 26 located around incandescentlamp. Maximum chromatic aberration AC reaches to 0.0477. SRLER ofspectrum of four-package LEDs are inverse proportion function oft shownin FIG. 27 ranging from 0.13 to 0.22, and relative difference reaches toabout 70%.

$\begin{matrix}\left\{ \begin{matrix}{{f(t)}_{R} = t} \\{{f(t)}_{A} = {{{- 1.18783}*t} + 0.761926}} \\{{f(t)}_{G} = {{0.326112*t} + 0.119559}} \\{{f(t)}_{B} = {{{- 1.13828}*t} + 0.118516}}\end{matrix} \right. & (31)\end{matrix}$

The examples set forth above are provided to give those of ordinaryskill in the art a complete disclosure and description of how to makeand use the embodiments of the arrangements, devices, compositions,systems and methods of the disclosure, and are not intended to limit thescope of what the inventors regard as their disclosure. All patents andpublications mentioned in the specification are indicative of the levelsof skill of those skilled in the art to which the disclosure pertains.

The entire disclosure of each document cited (including patents, patentapplications, journal articles, abstracts, laboratory manuals, books, orother disclosures) in the Background, Summary, Detailed Description, andExamples is hereby incorporated herein by reference. All referencescited in this disclosure are incorporated by reference to the sameextent as if each reference had been incorporated by reference in itsentirety individually. However, if any inconsistency arises between acited reference and the present disclosure, the present disclosure takesprecedence.

The terms and expressions which have been employed herein are used asterms of description and not of limitation, and there is no intention inthe use of such terms and expressions of excluding any equivalents ofthe features shown and described or portions thereof, but it isrecognized that various modifications are possible within the scope ofthe disclosure claimed Thus, it should be understood that although thedisclosure has been specifically disclosed by preferred embodiments,exemplary embodiments and optional features, modification and variationof the concepts herein disclosed can be resorted to by those skilled inthe art, and that such modifications and variations are considered to bewithin the scope of this disclosure as defined by the appended claims.

It is also to be understood that the terminology used herein is for thepurpose of describing particular embodiments only, and is not intendedto be limiting. As used in this specification and the appended claims,the singular forms “a,” “an,” and “the” include plural referents unlessthe content clearly dictates otherwise. The term “plurality” includestwo or more referents unless the content clearly dictates otherwise.Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the disclosure pertains.

When a Markush group or other grouping is used herein, all individualmembers of the group and all combinations and possible subcombinationsof the group are intended to be individually included in the disclosure.Every combination of components or materials described or exemplifiedherein can be used to practice the disclosure, unless otherwise stated.One of ordinary skill in the art will appreciate that methods, deviceelements, and materials other than those specifically exemplified can beemployed in the practice of the disclosure without resort to undueexperimentation. All art-known functional equivalents, of any suchmethods, device elements, and materials are intended to be included inthis disclosure. Whenever a range is given in the specification, forexample, a temperature range, a frequency range, a time range, or acomposition range, all intermediate ranges and all subranges, as wellas, all individual values included in the ranges given are intended tobe included in the disclosure. Any one or more individual members of arange or group disclosed herein can be excluded from a claim of thisdisclosure. The disclosure illustratively described herein suitably canbe practiced in the absence of any element or elements, limitation orlimitations that is not specifically disclosed herein.

A number of embodiments of the disclosure have been described. Thespecific embodiments provided herein are examples of useful embodimentsof the disclosure and it will be apparent to one skilled in the art thatthe disclosure can be carried out using a large number of variations ofthe devices, device components, methods steps set forth in the presentdescription. As will be obvious to one of skill in the art, methods anddevices useful for the present methods can include a large number ofoptional composition and processing elements and steps. In particular,it will be understood that various modifications may be made withoutdeparting from the spirit and scope of the present disclosure.Accordingly, other embodiments are within the scope of the followingclaims.

It should be also appreciated that the above described methodembodiments may take the form of computer or controller implementedprocesses and apparatuses for practicing those processes. The disclosurecan also be embodied in the form of computer program code containinginstructions embodied in tangible media, such as floppy diskettes,CD-ROMs, hard drives, or any other computer-readable storage medium,wherein, when the computer program code is loaded into and executed by acomputer or controller, the computer becomes an apparatus for practicingthe invention. The disclosure may also be embodied in the form ofcomputer program code or signal, for example, whether stored in astorage medium, loaded into and/or executed by a computer or controller,or transmitted over some transmission medium, such as over electricalwiring or cabling, through fiber optics, or via electromagneticradiation, wherein, when the computer program code is loaded into andexecuted by a computer, the computer becomes an apparatus for practicingthe invention. When implemented on a general-purpose microprocessor, thecomputer program code segments configure the microprocessor to createspecific logic circuits.

While the invention has been described with reference to a preferredembodiment, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from theessential scope thereof. Therefore, it is intended that the inventionnot be limited to the particular embodiment disclosed as the best modecontemplated for carrying out this invention, but that the inventionwill include all embodiments falling within the scope of the appendedclaims.

1. A method implemented on a computing device having at least oneprocessor, at least one computer-readable storage medium, and acommunication port for providing artificial lighting under a workingcondition, the method comprising: determining a destined chromaticity ofa destined light; selecting one or more component lights, each componentlight having a component chromaticity; determining a proportion for eachcomponent light based on a function including at least one optimizationparameter associated with a circadian rhythm of a subject, the at leastone optimization parameter having a first functional correlation withthe proportion of at least one component light; and combining the one ormore component lights according to the one or more proportions, therebysynthesizing the destined light.
 2. The method of claim 1, wherein thefirst functional correlation is a linear function, an inverse function,an exponential function, a logarithmic function, a power function or aregular non-linear function.
 3. The method of claim 1, wherein the oneor more proportions of the one or more component lights assumes a secondfunctional correlation with respect to one another each other.
 4. Themethod of claim 2, wherein the second functional correlation is a linearfunction or a multivariate function.
 5. The method of claim 1, whereinbefore the determining a destined chromaticity of a destined light, themethod further comprises acquiring information of the working condition.6. The method of claim 5, wherein the information is one or moreselected from the group consisting of a reflectance spectrum of a targetobject, color appearance of a target object under the artificiallighting, a condition of an ambient environment, and a purpose of theartificial lighting.
 7. The method of claim 6, wherein the reflectancespectrum of the target object depends on a spectrum power distributionof the destined light and a spectral reflectance curve of the targetobject.
 8. The method of claim 5, wherein the acquiring information ofthe working condition is performed by receiving the information via auser input or detecting the information via a sensor.
 9. The method ofclaim 1, wherein before the determining a proportion for each componentlight, the method further comprises selecting the at least oneoptimization parameter.
 10. The method of claim 9, wherein the at leastone parameter is selected from the group consisting of luminousefficacy, luminous efficacy of radiation, color rendering index, colortemperature, circadian efficacy of radiation, mesopic efficacy ofradiation, luminous efficacy in scotopic vision, spectral reflectanceluminous efficacy of radiation, photosynthetic photon flux, andchromaticity of light reflected by a target object.
 11. The method ofclaim 1, wherein the function is configured to afford differentialweight to the at least one optimization parameter.
 12. The method ofclaim 1, wherein the determining a destined chromaticity of a destinedlight is based on a chromaticity of light reflected by a target objectunder the artificial illumination.
 13. The method of claim 1, whereinthe one or more component lights include four component lights.
 14. Themethod of claim 1, wherein at least one of the one or more componentlights is monochromatic or polychromatic.
 15. A system for providing anartificial lighting under a working condition, the system comprising: aplurality of light sources, each light source capable of emitting acomponent light having a component chromaticity; a computer-readablestorage medium storing executable instructions, and at least oneprocessor in communication with the computer-readable storage medium,when executing the executable instructions, causing the system toimplement a method, comprising: determining a destined chromaticity of adestined light; selecting one or more component lights, each componentlight having a component chromaticity; determining a proportion of eachselected component light based on a function including at least oneoptimization parameter associated with a circadian rhythm of a subject,the at least one optimization parameter having a first functionalcorrelation with the proportion of at least one component light; andcombining the one or more component lights according to the one or moreproportions, thereby synthesizing the destined light.
 16. (canceled) 17.The system of claim 15, wherein the system is caused to implement themethod further comprising acquiring working condition informationincluding a reflectance spectrum of a target object detected by one ormore sensors.
 18. (canceled)
 19. The system of claim 17, wherein theworking condition information further comprises luminance, color,temperature, weather, climate, or time of an ambient environment.20.-22. (canceled)
 23. The system of claim 17, wherein the firstfunctional correlation is defined according to the working conditioninformation. 24.-25. (canceled)
 26. The system of claim 15, wherein atleast one of the plurality of light sources is a LED, a polychromaticLED, a multi-packaged LED, a phosphor-converted LED, a high pressuresodium lamp, or a fluorescent lamp.
 27. The system of claim 15, whereinthe system is caused to implement the method further comprisingcontrolling a magnitude of current or voltage delivered to each lightsource, thereby individually controlling an amount of component lightemitted by the corresponding light source.